Tag
The paper proposes PX-UAP, a method using probabilistic robustness and explainable AI to generate universal adversarial perturbations against deep reinforcement learning-based intrusion detection systems, demonstrating improved attack effectiveness in experiments.
A comprehensive guide for securing Linux servers, covering SSH hardening, firewall configuration with UFW, and intrusion detection using tools like Fail2Ban and CrowdSec.
This empirical study compares conversational XAI (powered by LLMs) against a traditional dashboard for UAV intrusion detection auditing, finding the conversational interface improves perceived usefulness but risks operator over-reliance on AI advice.
This paper introduces SemiScope, an analysis tool designed to disentangle the effects of classifier tuning from joint SSL and classifier optimization in semi-supervised security classification. Results show that most performance gains attributed to joint optimization can be recovered by simply tuning the classifier and its decision threshold with Bayesian optimization.
This paper investigates the cross-domain generalization failure of lightweight ML models for IIoT intrusion detection, finding they rely on coarse port features and that adversarial robustness does not correlate with cross-network performance.
EdgeDetect is a federated intrusion detection system for 6G-IoT environments that combines importance-aware gradient binarization (32× compression) with Paillier homomorphic encryption to achieve 98% accuracy on CIC-IDS2017 while reducing communication overhead by 96.9% and enabling deployment on resource-constrained devices like Raspberry Pi 4.